5 citations · 9 across the 12 of their papers we have counts for
6 papers · 1 filter
MAP: Domain Generalization via Meta-Learning on Anatomy-Consistent Pseudo-Modalities
Dewei Hu, Hao Li, Han Liu +3
Deep models suffer from limited generalization capability to unseen domains, which has severely hindered their clinical applicability. Specifically for the retinal vessel segmentat…
False Negative/Positive Control for SAM on Noisy Medical Images
Xing Yao, Han Liu, Dewei Hu +9
The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixe…
VesselMorph: Domain-Generalized Retinal Vessel Segmentation via Shape-Aware Representation
Dewei Hu, Hao Li, Han Liu +3
Due to the absence of a single standardized imaging protocol, domain shift between data acquired from different sites is an inherent property of medical images and has become a maj…
CATS v2: Hybrid encoders for robust medical segmentation
Hao Li, Han Liu, Dewei Hu +3
Convolutional Neural Networks (CNNs) have exhibited strong performance in medical image segmentation tasks by capturing high-level (local) information, such as edges and textures.…
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation
Han Liu, Hao Li, Xing Yao +6
Medical image segmentation is a critical task in medical image analysis. In recent years, deep learning based approaches have shown exceptional performance when trained on a fully-…
Deep Angiogram: Trivializing Retinal Vessel Segmentation
Dewei Hu, Xing Yao, Jiacheng Wang +2
Among the research efforts to segment the retinal vasculature from fundus images, deep learning models consistently achieve superior performance. However, this data-driven approach…